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Information and Communication Technology [ICT] with Digital India initiative aims at bringing major revolutionary changes in rural India with focus on developing “Smart Systems”. The Government of India under ministry of Information Technology has launched numerous schemes to promote use of IT in rural area and especially in agriculture sector. Even after regular efforts, the problems are still persisting, due to non availability of regular resources like: power, environmental resources and trained manpower. In agricultural sector farmers suffers due to electricity and irregular weather conditions. This reduces the overall crop production and affects the economy & development of country. The major problems can be classified as: over / under irrigation of crops, non-availability of electric power to operate the water pumps as and when required. The research work presented in the paper is an attempt to design an application to provide solution to farmers and ease the hectic life style. The solution designed is an android based application, with support of “native” language. The presented work is based on Mobile system to fascinate the farmers for operation of day-to-day activities like: motor pump operation, decision making based on weather information, data and message sharing related with crucial activities involved in farming. The system operates irrespective of global location of farmer and equipments.

ICT, Agriculture, Smart Systems, mobile ecosystem

[1]. Effect of chemical fertilizer on physic chemical characteristics of soil samples, J.Divya, The Ecoscan -2012
[2]. Weather Information for Sustainable Agriculture in India, L.S.Rathore, Journal of Agriculture Physics, - 2013.
[3]. Introduction to Arduino-A piece of cake, Alan G.Smith – 2011.
[4]. Exploring IoT applications using Raspberry Pi, C.W.Zhao et al, International Journal of Computer Networks and applications, Vol.2, 2015.
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M.B. Chandak, "Role of ICT in developing Smart Agriculture Systems: Digital India Initiatives," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.124-128, 2018.

The purpose of this project is to implement a VR game in which the EEG headset is trained to respond to the stimuli within the game by capturing a player’s EEG signals. These EEG signals are generated by the player’s emotions such as attention and meditation. Using the values obtained from the EEG headset we will enable the player to control the game using the headset. The main objective of this project is to empower a person with the ability to control any object in a virtual environment by analysing brain wave patterns and applying the resultant data to train the Brain Control Interface(BCI) device. The vision of the project is to make a player free from using hardware plug-ins and control the game using BCI.

EEG, BCI Headset, VR Headset, Unity

[1] Banghua Yang, Tao Zhang, Kaiwen Duan “Development of a BCI Simulated Application System Based on Unity3D” Virtual Reality and Visualisation ( ICVRV) ,2015 International Conference
[2] C. Umale, A. Vaidya “Feature Extraction Techniques and Classification Algorithms for EEG Signals to detect Human Stress - A Review” International Journal of ComputerApplications Technology and Research, Volume 5 – Issue.1 ,08-4-2016.
[3] Sadiq J. Abou-Loukh, Arwa Ra`ad Obaid “Comparison of Preprocessing Algorithms using anAffordable EEG Headset”, International Journal of Computer Applications, Volume 160- No 1,February 2017
[4] “EEG control of devices using sensory evoked potentials” patent no.US 8155736 B2
[5] “Sensory-evoked potential (SEP) classification/detection in the time domain patent no US 8391966 B2
[6] Perception based predictive tracking for head mounted displays patent no CA 2912109 A1
[7] Karolina Holewa, Agata Nawrocka “Emotiv EPOC neuroheadset in Brain – Computer Interface” Control Conference (ICCC),2014 15th International Carpathian
[8] C. Guger, C. Holzner, C. Grönegress, G. Edlinger, M. Slater “Brain-Computer Interface for Virtual Reality Control” European Symposium On Artificial Neural Networks(ESAAN) , 22-24 April 2009.
[9] Fabien Lotte, Josef Faller, Christoph Guger, Yann Renard, Gert Pfurtscheller, Anatole Lécuyer , Robert Leeb. “Combining BCI with Virtual Reality: Towards New Applications & Improved BCI” ,Toward Practical Brain Computer Interfaces
[10] Doron Friedman “Brain-Computer Interfacing and Virtual Reality”,Handbook of Digital Games and Entertainment Technologies
[11] Swati Vaid, Preeti Singh “EEG SIGNAL ANALYSIS FOR BCI INTERFACE: AREVIEW”,Advanced Computing & communication Technologies (ACCT) ,2015 5th International Conference

Y. Galphat, M. Gangwani, A. Bhave, B.S. Chadha, S. Adnani, "Integrating BCI with Virtual Reality," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.129-131, 2018.

Ontology defines a common vocabulary which includes machine interpretable definitions of basic concepts in the domain and relation among them for researchers who need to share information in a domain. But in actual, in place of reusing existing ontologies of required domain, domain experts create their own ontology leading in formation of multiple ontologies of the same domain containing incomplete concepts and relations. This causes ontology heterogeneity and inconsistency problem. For better and precise results managing, these heterogeneous ontologies are necessary. A large number of work has been done in the recent past for managing the existing ontologies so that they can be reused for data integration, information integration, data warehousing and other fields. This paper provides different approaches that have been used in the recent years for managing these ontologies.

Ontology, Ontology Merging, Ontology Alignment, Ontology Mapping, Ontology Matching

[1] P. Jain, P.Z. Yeh, K. Verma, R.G. Vasquez, M. Damova, P. Hitzler, Amit P. Sheth, “Contextual Ontology Alignment of LOD with an Upper Ontology: A Case Study with Proton”, In Proceedings of ESWC, 2011.
[2] Y.R. Jean-Marya , E.P. Shironoshita and M.R. Kabuka, “Ontology matching with semantic verification, Journal of Web Semantics: Science, Services and Agents on the World Wide Web”, 7(3) (2009) 235-251.
[3] J Gracia, J Bernad, E Mena, “Ontology Matching with CIDER: evaluation report for OAEI 2011”,Proceeding of Ontology Matching Workshop (OM`11), at 10th International Semantic Web Conference (ISWC`11), Bonn (Germany)
[4] J. Li, J. Tang, Y. Li, Q. Luo, “ RiMOM: A Dynamic Multistrategy Ontology Alignment Framework”, IEEE Transactions on Knowledge and Data Engineering 2009
[5] Sabine Massmann , Salvatore Raunich , David Aumüller , Patrick Arnold , Erhard Rahm, “Evolution of the COMA match system”, Proceedings of the 6th International Conference on Ontology Matching, p.49-60, October 24, 2011, Bonn, Germany.
[6] D. Ngo, Z. Bellahsene, “YAM++: A multi-strategy based approach for Ontology matching task”, In Proceedings of EKAW, 2012.
[7] Aroua Essayeha ,Mourad Abeda, “Towards ontology matching based system through terminological, structural and semantic level”, 19th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, Procedia Computer Science 60 ( 2015 ) 403 – 412
[8] Cheatham, M., Hitzler, P., “String similarity metrics for ontology alignment”. In ISWC 2013, Part II. LNCS, vol. 8219, pp. 294–309. Springer, Heidelberg (2013)
[9] J. Guang Zheng, L. Fu, X. Ma, P. Fox, “SEM+: tool for discovering concept mapping in Earth science related domain” Earth Science Information, 2015.
[10] Walid Hassen, “Medley results for OAEI 2012”, Proceedings of the 7th International Conference on Ontology Matching, p.168-172, November 11, 2012, Boston, MA
[11] C. Shao, L. Hu, J. Li Z. Wang, T. Chung, J. Xia, “RiMOM-IM: A Novel Iterative Framework for Instance Matching”, Journal of computer science and technology, 2016.
[12] D. McGuinness, R. Fikes, J. Rice, and S. Wilder, “The Chimaera Ontology Environment”, In Proceedings of the 17th National Conference on Artificial Intelligence (AAAI), 2000.
[13] S. Raunich, E. Rahm, “Target-driven merging of taxonomies with ATOM , Information Systems, 2014.
[14] P. Lambrix, H. Tan, “SAMBO - A System for Aligning and Merging Biomedical Ontologies”, Journal of Web Semantics, 2006.
[15] K. Kotis, G. A. Vouros, K. Stergiou, “Towards Automatic Merging of Domain Ontologies: The HCONE-merge approach, Journal of Web Semantics,” 2006.
[16] N. Noy and M. Musen, “The PROMPT Suite: Interactive tools for ontology merging and mapping”, International Journal of Human-Computer Studies, 2003.
[17] Nora Maiz, Muhammad Fahad ,Omar Boussaid, Fadlia Bentayeb, “Automatic Ontology Merging by Hierarchical Clustering and Inference Mechanisms” , proceeding of IKNOW (2010),pp.81-93
[18] Lacasta, J., Nogueras-Iso, J., Zarazaga-Soria, F.J., Muro-Medrano, P. “Generating an urban domain ontology through the merging of cross-domain lexical ontologies”, in Proceedings of Second Towntology Workshop “Ontologies for Urban Development: Conceptual Models for Practitioners,” 17, 18 Oct 2007. Castello del Valentino, Turin, Italy (2007)
[19] Marília T. de Mello, Mara Abel Francisco García-Sánchez, “Using Semantic Web Services to Integrate Data and Processes from Different Web Portals”, International Workshop on Intelligent Web Based Tools (IWBT-07) in conjunction with 19th IEEE ICTAI-07

Ranjna Jain, Neelam Duhan, A.K.Sharma, "Comparative Study on Ontology Management Approaches in Semantic Web," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.132-140, 2018.

VANET has become one of the active areas of research and development these days. As communication among the vehicles is one of the challenging tasks in a highly dynamic environment. Many of the clustering algorithms have been implemented for providing the secured communication between vehicles within a cluster. Generally Clustering consists of different nodes like mobile devices, vehicles, sensors, etc. that are grouped together according to some predefined rules. A group of nodes within a cluster can communicate with each other securely and without disconnection. The important task of the clustering algorithm is to achieve the cluster stability. Here different categories of clustering algorithm are highlighted, such as the significance of various approaches with their objectives

VANET, Classification of clustering, Clustering techniques

[1] Sivasakthi M, Suresh S.R, “Research on vehicular Ad-hoc networks (VANETs): An overview”, IJASER, Vol. 2, No. 1, pp. 23-27, 2013.
[2] Md. Humayun Kabir,” Research Issues on Vehicular Ad-hoc Network”, International Journal of Engineering Trends and Technology (IJETT) –Vol. 6, No. 4 , pp. 174-179, Dec 2013.
[3] Weiwei Li, Ali Tizghadam, and Alberto Leon-Garcia, “Robust clustering for connected vehicles using local network criticality”, In Communications (ICC), 2012 IEEE International Conference , Ottawa, ON, Canada , pp. 7157-7161. IEEE, 2012.
[4] Taek Jin Kwon, Mario Gerla, Vijay K Varma, Melbourne Barton, and T Russell Hsing, ”Efficient flooding with passive clustering-an overhead-free selective forward mechanism for ad hoc/sensor networks”, Proceedings of the IEEE, Vol. 91, Issue. 8, pp. 1210-1220, Aug 2003.
[5] Ismail Salhi, Mohamed Cherif, and S Senouci, “ Data collection in vehicular networks”, In Proc. ASN symposium, 2007.
[6] Zhigang Wang, Lichuan Liu, MengChu Zhou, and Nirwan Ansari, ”A position-based clustering technique for ad hoc intervehicle communication”, Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, Vol. 38, Issue.2 , pp.201- 208, 2008.
[7] Nitin Maslekar, Mounir Boussedjra, Joseph Mouzna, and Houda Labiod, “A stable clustering algorithm for efficiency applications in vanets” , In Wireless Communications and Mobile Computing Conference (IWCMC), 2011 7th International, Istanbul, Turkey , pp. 1188-1193. IEEE, 2011.
[8] Grzegorz Wolny, “Modified dmac clustering algorithm for vanets” In Systems and Networks Communications, 2008. ICSNC`08. 3rd International Conference on Systems and Networks Communications, Washington, DC, USA, vol.4, Issue.3, pp. 268-273. IEEE, 2008 .
[9] Stefano Basagni. “ Distributed clustering for ad hoc networks” In Parallel Architectures, Algorithms, and Networks, 1999.(I-SPAN`99) Proceedings. Fourth International Symposium on, Perth/Fremantle, Australia,pp.310-315. IEEE, 1999.
[10] Wei Fan, Yan Shi, Shanzhi Chen, and Longhao Zou,” A mobility metrics based dynamic clustering algorithm for vanets “ , In Communication Technology and Application (ICCTA 2011), IET International Conference , Beijing, China, pp. 752-756. IET, 2011.

[11] Nitin Maslekar, Joseph Mouzna, Houda Labiod, Manoj Devisetty, and Ming-Chyi Pai, “Modified cdrive: Clustering based on direction in vehicular environment” ,in Intelligent Vehicles Symposium (IV), 2011 IEEE, pp. 845-850. IEEE, 2011 .

[12] Wang Xiaonan and Qian Huanyan, “ Constructing a vanet based on cluster chains” , International Journal of Communication Systems,Vol. 27, Issue.11, pp. 2497-2517, 2014.

[13] Behnam Hassanabadi, Christine Shea, L Zhang, and Shahrokh Valaee, “Clustering in vehicular ad hoc networks using affinity propagation” , Ad Hoc Networks,Vol. 13, pp. 535-548, 2014.

[14] Farhan Ahammed, Javid Taheri, and Albert Zomaya, “ Lica: robust localization using cluster analysis to improve gps coordinates” In Proceedings of the first ACM international symposium on Design and analysis of intelligent vehicular networks and applications, Miami, Florida, USA, pp. 39-46. ACM, 2011.

[15] Daxin Tian, Yunpeng Wang, Guangquan Lu, and Guizhen Yu “A vanets routing algorithm based on euclidean distance clustering” In Future Computer and Communication(ICFCC), 2010 2nd International Conference , Wuhan, China, vol. 1, pp. V1-183.IEEE, 2010.

[16] Mildred M Caballeros Morales, Choong Seon Hong, and Young-Cheol Bang, “An adaptable mobility-aware clustering algorithm in vehicular networks”, in Network Operations and Management Symposium (APNOMS), 2011 13th Asia-Pacific, Taipei city, Taiwan, pp. 1-6. IEEE, 2011.

[17] RA Santos, RM Edwards, and NL Seed, “Inter vehicular data exchange between fast moving road traffic using an ad-hoc cluster-based location routing algorithm and 802.11 b direct sequence spread spectrum radio” , in Post Graduate Networking Conference, Liverpool, UK, 2003.
[18] Peng Fan, “Improving broadcasting performance by clustering with stability for inter-vehicle communication” In Vehicular Technology Conference, 2007. VTC2007- Spring. IEEE 65th, Dublin, Ireland pp. 2491-2495. IEEE, 2007.

[19] Mohammad S. Almalag and Michele C. Weigle, “Using Traffic Flow for Cluster Formation in Vehicular Ad-hoc Networks”, IEEE 35th Conference on Local Computer Networks (LCN), Denver, CO, USA,pp. 631-636, 2010.

[20] Zhang, Z., A. Boukerche, and R. Pazzi, ”A novel multihop clustering scheme for vehicular ad-hoc networks” in Proceedings of the 9th ACM international symposium on Mobility management and wireless access. Miami, Florida, USA. pp. 19-26, 2011. ACM.

[21] Vaishali Jain, Rajendra Singh Kushwah, ”Review of Various VANET Protocols Using NS-2 Simulator”, International Journal of Computer Sciences and Engineering, Vol.-4, Issue.7, PP.76-80, Jul 2016

Megha Rani, Akash Saxena, "Clustering Techniques Used in Vehicular Ad-hoc Network: A Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.141-145, 2018.

Todays scenario conversion of data from the databases or data warehouse to avail the users is one of the challenging tasks in data mining. There is high risk of data loss and these losses of data sometimes create high risk for users for their sensitive data; because large amount of data gets publish on daily basis. Data mining comes now a day has lots of necessary techniques for privacy preserving. In the past decennary the evolution of various data mining techniques, privacy preservation in data mining becomes an important issues. Basically privacy preservation of data mining provides the facility of sharing of critical data for analysis purposes. The problem of privacy preserving data mining becomes very crucial due to the possibility of occurrence of personal data. Essential parameter used for preserving the privacy of data mining is efficiency, time, cost, accuracy. To achieve the high privacy user have to compromise accuracy, time and cost. This survey paper mainly discussed the introduction of Data Mining, some of the proposed algorithm for privacy preserving in data mining and framework of privacy preservation. Several privacy preservation techniques in data mining based upon different parameters to measure Information Loss Rate (ILR) and Privacy Ratio (PR) are also discussed in this paper.

Data Mining, Privacy preserving Data Mining, Clustering

[1] R Natarajan, "A survey on Privacy Preserving Data Mining, "International Journal of Advanced Research in Computer and Communication Engineering Vol.1, Issue.1, pp. 103-112, 2012.
[2] Kalita, M., D. K. Bhattacharyya, and M. Dutta,"Privacy Preserving Clustering-A Hybrid Approach." Advanced Computing and Communications, 2008. ADCOM 2008. 16th International Conference, Chennai, India. IEEE, 2008.
[3] Shikha Sharma & Pooja Jain, “A Novel Data Mining Approach for Information Hiding”, International Journal of Computers and Distributed Systems, Vol.1, Issue 3, October 2012.
[4] Arpit Agrawal,” Security based Efficient Privacy Preserving Data Mining” International Journal of Application or Innovation in Engineering & Management, Vol. 2, Issue 7, pp- 225,July 2013.
[5] Tagaram Soni Madhulatha, "Compa rison between K-Means and K-Medoids Clustering Algorithms", Communications in Computer and Information Science, vol. 198, pp. 472-481, 2011.
[6] Zhang L, Yang M, Lei D. “An improved PAM clustering algorithm based on initial clustering centers” 2012. Applied Mechanics and Materials, Vol.135-136, pp. 244-249, 2012.
[7] A. Susana, Leiva-Valdebenito, J. Francisco, Torres-Aviles, “ A review of the most common partition algorithms in cluster analysis: a comparative study”, Colombian Journal of Statistics , Vol. 33, No. 2, pp.321–339, 2010.
[8] Bhat, A., “K-Medoids Clustering using Partitioning Around Medoids for Performing Face recognition”, Inter -national Journal of Soft Computing, Mathemetics and Control(IJSCMC), Vol. 3, No. 3, pp. 1-12. August 2014.
[9] Md Zahidul Islam, Ljiljana Brankovic “Privacy preserving data mining: A noise addition framework using a novel clustering technique”, Elsevier, Vol. 24, Issue. 8, pp. 1214-1223, 2011 .
[10] Ratna Kendhe, Lahar Mishra, Janhavi Bhalerao,"Privacy Preserving In Data Mining: A Survey", International Journal of Scientific and Research Publications, Vol 5, Issue 10, pp.1-4 , October 2015.
[11] V. Rajalakshmi, ”Anonymization based on nested clustering for privacy preservation in data mining”, Indian Journal of Computer Science and Engineering (IJCSE), Vol. 4 ,No.3 ,Jun-Jul 2013.

Smita Rani, Akash Saxena, "Privacy Preserving In Data Mining: A Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.146-150, 2018.

Fog computing Provides extending feature for proper Infrastructure, Storage, Computation and services that bring cloud to the edge of network theory, fortunately or unfortunately cloud-Iot Aches from various issues such as network latency, Volume of data uploaded and the data being accessed, privacy and security. There are many schemes and methods that giving the solutions for the research being carried out in iot security, however the practical issues are still exist and in this paper the various schemes such as Distributed computing, Edge computing, homomorphic encryption, fine grained privacy preservation technique, attribute based encryption and other schemes which relates to security are reviewed and check the nature of cloud-iot based encryption schemes, In the meantime the analogy of secure access for proposed schemes such as confidentiality, availability, privacy and Integrity that securely meet the requirements of security in fog and internet of things. The Methodology is find to be metric and generic and it possess a wide range of applications and topologies with respect to networking and security. Now shall be addressing the various research techniques in order to review the security concerns in fog, cloud-IoT

Distributed computing, key delegation, Edge computing, CP-ABE, homomorphic encryption, Hierarchical abe, fine grained privacy preservation technique

[1] Chen, Y. C., Chang, Y. C., Chen, C. H., Lin, Y. S., Chen, J. L., & Chang, Y. Y. (2017, May). Cloud-fog computing for information-centric Internet-of-Things applications. In Applied System Innovation (ICASI), 2017 International Conference on (pp. 637-640). IEEE.
[2] Alotaibi, Asma, Ahmed Barnawi, and Mohammed Buhari. "Attribute-Based Secure Data Sharing with Efficient Revocation in Fog Computing." Journal of Information Security 8.03 (2017): 203.
[3] Koo, D., Shin, Y., Yun, J., & Hur, J. (2016, December). A Hybrid Deduplication for Secure and Efficient Data Outsourcing in Fog Computing. In Cloud Computing Technology and Science (CloudCom), 2016 IEEE International Conference on (pp. 285-293). IEEE.
[4] Su, J., Cao, D., Zhao, B., Wang, X., & You, I. (2014). ePASS: An expressive attribute-based signature scheme with privacy and an unforgeability guarantee for the Internet of Things. Future Generation Computer Systems, 33, 11-18.
[5] Deng, R., Lu, R., Lai, C., Luan, T. H., & Liang, H. (2016). Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE Internet of Things Journal, 3(6), 1171-1181.
[6] Liu, Y., Dong, B., Guo, B., Yang, J., & Peng, W. (2015). Combination of cloud computing and internet of things (IOT) in medical monitoring systems. International Journal of Hybrid Information Technology, 8(12), 367-376.
[7] Khairnar, Sonali, and Dhanashree Borkar. "Fog Computing: A New Concept To Minimize The Attacks And To Provide Security In Cloud Computing Environment." IJRET: International Journal of Research in Engineering and Technology 3.06 (2014).
[8] Zouari, Jaweher, Mohamed Hamdi, and Tai-Hoon Kim. "A privacy-preserving homomorphic encryption scheme for the Internet of Things." Wireless Communications and Mobile Computing Conference (IWCMC), 2017 13th International. IEEE, 2017.
[9] Yang, Xue, Fan Yin, and Xiaohu Tang. "A Fine-Grained and Privacy

-Preserving Query Scheme for Fog Computing-Enhanced Location-Based Service." Sensors 17.7 (2017): 1611.
[10] Yang, Lei, Abdulmalik Humayed, and Fengjun Li. "A multi-cloud based privacy-preserving data publishing scheme for the internet of things." Proceedings of the 32nd Annual Conference on Computer Security Applications. ACM, 2016.
[11] Vishwanath, Akhilesh, Ramya Peruri, and Jing (Selena) He. Security in fog computing through encryption. DigitalCommons@ Kennesaw State University, 2016.
[12] Hu, P., Ning, H., Qiu, T., Song, H., Wang, Y., & Yao, X. (2017). Security and privacy preservation scheme of face identification and resolution framework using fog computing in internet of things. IEEE Internet of Things Journal.
[13] Jiang, Y., Susilo, W., Mu, Y., & Guo, F. (2017). Ciphertext-policy attribute-based encryption against key-delegation abuse in fog computing. Future Generation Computer Systems.
[14] Huang, Qinlong, Licheng Wang, and Yixian Yang. "DECENT: Secure and fine-grained data access control with policy updating for constrained IoT devices." World Wide Web (2017): 1-17.
[15] Alrawais, A., Alhothaily, A., Hu, C., Xing, X., & Cheng, X. (2017). An Attribute-Based Encryption Scheme to Secure Fog Communications. IEEE Access.
[16] Huang, Qinlong, Yixian Yang, and Licheng Wang. "Secure Data Access Control With Ciphertext Update and Computation Outsourcing in Fog Computing for Internet of Things." IEEE Access 5 (2017): 12941-12950.
[17]Taneja, Mohit, and Alan Davy. "Resource aware placement of IoT application modules in Fog-Cloud Computing Paradigm." Integrated Network and Service Management (IM), 2017 IFIP/IEEE Symposium on. IEEE, 2017.
[18] Lu, R., Heung, K., Lashkari, A. H., & Ghorbani, A. A. (2017). A Lightweight Privacy-Preserving Data Aggregation Scheme for Fog Computing-Enhanced IoT. IEEE Access, 5, 3302-3312.
[19] Luigi Atzori a, Antonio Iera b, Giacomo Morabito c,* The Internet of Things: A survey, Computer Networks 54 (2010) 2787–2805
[20] Dan Boneh, Matthew Frankliny, Identity-Based Encryption from the Weil Pairing Appears in SIAM J. of Computing, Vol. 32, No. 3, pp. 586-615, 2003.
[21] Mrinmoy Barua*, Xiaohui Liang, Rongxing Lu and Xuemin Shen, ESPAC: Enabling Security and Patient-centric Access Control for eHealth in cloud computing, Int. J. Security and Networks, Vol. 6, Nos. 2/3, 2011

Ravula Arun Kumar, Kambalapally Vinuthna, "A Review on various secure data access schemes and techniques in Fog for Internet of Things," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.151-155, 2018.

Mobile Ad-Hoc Network is a self-configured collection of cellular nodes in which there is no need of predefined infrastructure. In this network nodes can arbitrarily alternate their geographic places. MANET is more vulnerable to cyber-attacks than wired networks because of no any central coordination mechanism. Because of their dynamic topology, no infrastructure and no central management system MANETs are liable to various security attacks. In this paper we have proposed a solution to detect and prevent multiple attacks in a network and find a secure way to transfer data from source to destination node. This article briefly discusses about the concept of Mobile Ad Hoc Network (MANET) and its various types of attack and methods to solve the MANET attacks.

Ad-hoc network, Infrastructure less network , Dynamic topology, Security attacks

[1] J.H.Schiller,Mobile communications ,Pearson Education,2003
[2] Nguyen, H.L.; Nguyen, U.T.: A study of different types of attacks on multicast in mobile ad hoc networks. Ad Hoc Netw. 61,32–46(2008)
[3] Panaousis, E.A.; Politis, C.: Securing ad hoc networks in extreme emergency cases. In: Proceedings of the World Wireless Research Forum, Paris (2009).
[4]Anil Lamba1, SohanGarg2, Rajeev Kumar3, A Literature Review of MANET’s Routing Protocols Along With Security Issues, 2016 IJSRSET | Volume 2 | Issue 6 | Print ISSN: 2395-1990 | Online ISSN : 2394-4099.
[5] Anurag Gupta, Kamlesh Rana, “Assessment of Various Attacks on AODV in Malicious Environment” 2015 1st International Conference on Next Generation Computing Technologies (NGCT-2015) Dehradun, India, 4-5 September 2015.
[6] Payal N. Raj, Prashant B. Swadas” DPRAODV: A Dyanamic Learning System Against Blackhole Attack in AODV Based Manet.” International Journal of Computer Science Issues, Vol. 2, 2009 ISSN (Online): 1694-0784 ISSN (Printed): 1694-0814.
[7] Vani A. Hiremani, Manisha Madhukar Jadhao, “Eliminating Co-operative Black hole and Gray hole Attacks Using Modified EDRI Table in MANET” IEEE ,2013.
[8] Durgesh Kshirsagar, Ashwini Patil, “Black hole Attack Detection and Prevention by Real Time Monitoring” Green Computing, Communication and Conservation of Energy (ICGCE), 2013 International Conference on 12-14 Dec. 2013
[9]Parvinder Kaur1. Dalveer Kaur2. Rajiv Mahajan3.Wormhole Attack Detection Technique in Mobile Ad Hoc Networks,Wireless Personal Communications November 2017, Volume 97, Issue 2, pp 2939–2950.
[10] Nirbhay Chaubey, Akshai Aggarwal, Savita Gandhi, Keyurbhai A Jani, “Performance Analysis of TSDRP and AODV Routing Protocol under Black Hole Attacks in MANETs by Varying Network Size” 2015 Fifth International Conference on Advanced Computing & Communication Technologies.
[11] Zahra Hosseini1. Zeinab Movahedi2.A Trust-Distortion Resistant Trust Management Scheme on Mobile Ad Hoc Networks. Wireless Personal Communications October 2017, Volume 96, Issue 4, pp 5167–5183
[12] Neeraj Arya, Upendra singh, Sushma singh, “Detecting and Avoiding of Worm Hole Attack and Collaborative Black hole attack on MANET using Trusted AODV Routing Algorithm” IEEE International Conference on Computer, Communication and Control (IC4-2015).
[13] K. Geetha1 . N. Sreenath2.Detection of SYN Flooding Attack in Mobile Ad hoc Networks with AODV Protocol, Arabian Journal for Science and Engineering March 2016, Volume 41, Issue 3, pp 1161–1172.
[14] Seryvuth Tan, Keecheon Kim, “Secure Route Discovery for Preventing Black Hole Attacks on AODV-based MANETs” 2013 IEEE International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing.
[15] Jian-Hua Song, Fan Hong and Yu Zhang, “Effective Filtering Scheme against RREQ Flooding Attack in Mobile Ad Hoc Networks”, Seventh International Conference on Parallel and Distributed Computing, Applications and Technologies, pages 497-50.
[16] Opinder Singh1, Jatinder Singh. Ravinder Singh, Multi-level trust based intelligence intrusion detection system to detect the malicious nodes using elliptic curve cryptography in MANET, Cluster Computing ,pp 1–13
[17]Geetika Sharma1, Anupam Mittal2, Ruchi Aggarwal3,” Attacks on Ad hoc On-Demand Distance Vector Routing in MANET International Research Journal of Engineering and Technology (IRJET) e-ISSN: Volume: 03 Issue: 06 | June-2016.
[18]Sushma Kushwaha1, Prof. Vijay Lokhande2,Security in Wireless Mobile Ad-Hoc Network Nodes Using Novel Intrusion Detection System, DOI 10.4010/2016.777,ISSN 2321 3361 © 2016 IJESC
[19] Sachin D. Ubarhande. Dharmpal D. Doye. Prakash S. Nalwade. A Secure Path Selection Scheme for Mobile Ad Hoc Network. Wireless Personal Communications, Volume 97, Issue 2, pp 2087–2096
[20] Marti, S., Giuli, T. J., Lai, K., & Baker, M. (2000). Mitigating routing misbehavior in mobile ad hoc networks. In ACM Proceedings of the 6th annual international conference on Mobile computing and networking, pp. 255–265.

M. Selladevi, S. Duraisamy, "Survey Paper on Various Security Attacks In Mobile Ad Hoc Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.156-160, 2018.

Natural language processing is widely discussed and researched topic nowadays. As it is one of the oldest area of research in machine learning it is used in major fields such as machine translation speech recognition and text processing. Natural language processing has brought major breakthrough in the field of computation and AI. Various algorithms used for Natural language processing are mainly dependent on the recurrent neural network. Different text and speech processing algorithm are discussed in this review paper and their working is explained with examples. Results of various algorithms show the development done in this field over past decade or so. We have tried to differentiate between various algorithms and also its future scope of research. The Gap analysis between different algorithms is mentioned in the paper as well as the application of these various algorithms is also explained. Natural language processing has not attained perfection till date but continuous improvement done is the field can surely touch the perfection line. Different AI now use natural language processing algorithms to recognize and process the voice command given by user.

EOS: End of sentence, GO: Start decoding, PAD: Filler, Seq2Seq, UNK: Unknown; word not in vocabulary

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In this paper we present efficient resource allocation algorithm which give rise to economic models for job scheduling in distributed computing environmental. Existing schemes which schedule jobs in such a environment have their routes in searching of time slots in resource occupancy schedules which consider only the time slot sets. Our algorithm proposes a hybrid time slot search algorithm and configures each job in an efficient schedule.

Distributed, Reliability System, Ant Colony Optimization, Multi Criteria Decision

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The current decade of watermarking technique faced a problem of geometrical and some other attack. The minimization of security attack in watermarking technique is major issue. For the minimization of geometrical attack used various transformed based technique. The transform based watermarking technique used some well know function such as DWT, DCT and combination of more wavelet based transform function. Now a day used various authors feature selection based watermarking technique. The feature selection based watermarking technique gives better security strength in compression of another transform based technique. In this paper proposed a classification based watermarking technique and reduces the mitigation of geometrical attack for the process of watermark security strength. For the minimization of attack used correlation coefficient matrix for the processing of embedding and the process of embedding done by the pattern generation. The process of pattern generation used support vector machine. The support vector machine is classifier; it classifies the data on the biases of guidance.

Watermarking, Geometrical Attack, DCT, DWT, SVM.

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Ankita Agrawal, Anubha parajapati, "Mitigation of Geometrical Attack in Watermarking Technique Using Support Vector Machine," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.172-176, 2018.